code: def frombuffer(
      buffer: bytes,
      dtype: Optional[tf.DType] = float,
      count: Optional[int] = -1,
      offset: Optional[int] = 0,
  ) -> Union[tf.Tensor, tf.Variable]:
      if isinstance(buffer, bytearray):
          buffer = bytes(buffer)
      ret = tf.io.decode_raw(buffer, dtype)
      dtype = tf.dtypes.as_dtype(dtype)
      if offset > 0:
          offset = int(offset / dtype.size)
      if count > -1:
          ret = ret[offset : offset + count]
      else:
          ret = ret[offset:]
  
      return ret
  
imports: import numpy
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import tensorflow
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes
  from .None import backend_version
label_desc: Use image node_id 21 for calling a global function with example usage: frombuffer(buffer, dtype, count, offset) and returns: ret